Using Bots to Study Echo Chambers and Political Polarization

Chris Bail
Duke University

 

How does exposure to opposing views shape political polarization?

Hypothesis #1: Intergroup Contact

Hypothesis #1: Intergroup Contact

Hypothesis #2: Backfire Effects

Hypothesis #2: Backfire Effects

Hypothesis #2: Backfire Effects

Hypothesis #3: Assymetric Polarization

Hypothesis #3: Assymetric Polarization

Hypothesis #3: Assymetric Polarization

Hypotheses

 

Hypotheses

 

1) Intergroup contact will reduce political polarization

Hypotheses

 

1) Intergroup contact will reduce political polarization

2) Backfire effects will increase political polarization

Hypotheses

 

1) Intergroup contact will reduce political polarization

2) Backfire effects will increase political polarization

3) Conservatives will be more likely to exhibit backfire effects than liberals

Research Design

Eligibility Criteria

 

Eligibility Criteria

 

1) Must be living in the United States

Eligibility Criteria

 

1) Must be living in the United States

2) Must visit Twitter at least three times per week

Eligibility Criteria

 

1) Must be living in the United States

2) Must visit Twitter at least three times per week

3) Must describe themselves as either a Republican or a Democrat

Outcome: Ideological Consistency Scale

Outcome: Ideological Consistency Scale

 

1) “Stricter environmental laws and regulations cost too many jobs and hurt the economy.”

2) “Government regulation of business is necessary to protect the public interest.”

3) “Poor people today have it easy because they can get government benefits without doing anything in return.”

4) “Immigrants today strengthen our country because of their hard work and talents.”

5) “Government is almost always wasteful and inefficient.”

Outcome: Ideological Consistency Scale

 

6) “The best way to ensure peace is through military strength.”

7) “Racial discrimination is the main reason why many black people can't get ahead these days.”

8) “The government today can't afford to do much more to help the needy.”

9) “Business corporations make too much profit.”

10) “Homosexuality should be accepted by society.”

Treatment: Twitter Bots

 

Treatment: Twitter Bots

Measuring Treatment Compliance

The Cutest Animals on the Internet*

*according to my daughter

Substantive Compliance Measure

 

Substantive Compliance Measure

 

“Over the past three days, the [name of study's Twitter account here] retweeted a message about a philanthropist who gave a large amount of money to help people recover from a major disaster. How much money did this person donate?”

Control Variables

 

Control Variables

 

1) Frequency of Twitter use

Control Variables

 

1) Frequency of Twitter use

2) Strength of partisanship

Control Variables

 

1) Frequency of Twitter use

2) Strength of partisanship

3) Interest in current events

Control Variables

 

1) Frequency of Twitter use

2) Strength of partisanship

3) Interest in current events

4) Ideological homophily (online)

Control Variables

 

1) Frequency of Twitter use

2) Strength of partisanship

3) Interest in current events

4) Ideological homophily (online)

5) Ideological homophily (offline)

Control Variables

 

1) Frequency of Twitter use

2) Strength of partisanship

3) Interest in current events

4) Ideological homophily (online)

5) Ideological homophily (offline)

6) Demographics/SES (age, gender, income, education, region)

Control Variables

 

1) Frequency of Twitter use

2) Strength of partisanship

3) Interest in current events

4) Ideological homophily (online)

5) Ideological homophily (offline)

6) Demographics/SES (age, gender, income, education, region)

7) Many others…

Compliance

Compliance

Results

Effect of Following Bot for 1 Month

Effect of Following Bot for 1 Month

 

Caveats

Conclusions

 

My collaborators

Comments Welcome!

Attrition

Mechanisms?

Addressing Causal Interference

Addressing Causal Interference

Addressing Causal Interference

Identify Verification

Identify Verification

Summer Institutes in Computational Social Science

 

Power Analysis